A Novel Cooperation Scheme for Wireless Sensor Networks
Bibliographic record
Abstract
Diversity is an effective way to combat the impacts of channel fading by obtaining diversity gains and therefore, is attractive in the design of wireless sensor networks (WSNs) where energy efficiency is important. In this paper, we consider a cluster-based WSN in which the base station (BS) is located far from the sensor nodes and cluster heads cooperatively transmit information to the BS using decode and forward approach in a code-division multiple-access (CDMA) system. The cluster heads serve as both information sources and relays at the same time. The energy efficiency improved by the proposed approach is presented in this paper. How the system parameters such as bit rate, quality of service (QoS) and distance from the BS influence the scheme selection is given in this paper as well. Simulation results show that the proposed scheme saves energy significantly when BS is far from WSN and the QoS requirement is stringent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".